Article Per Year (5 Year)
p-Index From 2021 - 2026
3.746
P-Index
This Author published in this journals
All Journal Jurnal Edik Informatika : Penelitian Bidang Komputer Sains dan Pendidikan Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) IT JOURNAL RESEARCH AND DEVELOPMENT INOVTEK Polbeng - Seri Informatika JITK (Jurnal Ilmu Pengetahuan dan Komputer) PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Digital Zone: Jurnal Teknologi Informasi dan Komunikasi JOURNAL OF SCIENCE AND SOCIAL RESEARCH Building of Informatics, Technology and Science Jurnal J-PEMAS JAIA - Journal of Artificial Intelligence and Applications Jurnal Pemberdayaan Sosial dan Teknologi Masyarakat SATIN - Sains dan Teknologi Informasi Bulletin of Social Informatics Theory and Application Neraca: Jurnal Ekonomi, Manajemen dan Akuntansi JEBD The Indonesian Journal of Computer Science INOVTEK Polbeng - Seri Informatika New Economy
Susandri, Susandri
Universitas Lancang Kuning
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Social Sciences

Published : 35 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 35 Documents
Search

 1   2   3   4 
Evaluation of the Effect Of Regularization on Neural Networks for Regression Prediction: A Case Study of MLLP, CNN, and FNN Models Susandri; Zamsuri, Ahmad; Nasution, Nurliana; Ramadhani, Maya
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/m2rcsf96

Abstract

Regularization is an important technique for developing deep learning models to improve generalization and reduce overfitting. This study evaluated the effect of regularization on the performance of neural network models in regression prediction tasks using earthquake data. We compare Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), and Feedforward Neural Network (FNN) architectures with L2 and Dropout regularization. The experimental results show that MLP without regularization achieved the best performance (RMSE: 0.500, MAE: 0.380, R²: 0.625), although prone to overfitting. CNN performed poorly on tabular data, while FNN showed marginal improvement with deeper layers. The novelty of this study lies in a comparative evaluation of regularization strategies across multiple architectures for earthquake regression prediction, highlighting practical implications for early warning systems.
ANALISIS SENTIMEN PERUNDUNGAN TERHADAP GURU DENGAN MENGGUNAKAN METODE SUPPORT VECTOR MACHINE DAN NAÏVE BAYES Zamsuri, Ahmad; Nasution, Nurliana; Susandri, Susandri; Bimby, Novia Putri
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 8, No 4 (2025): November 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i4.4916

Abstract

Abstract: This study discusses sentiment analysis of bullying experienced by teachers on social media. The research employs the Support vector machine (SVM) and Naïve Bayes methods to classify sentiments into positive, negative, or neutral categories. The data were collected from various social media platforms and analyzed using text mining techniques. The results show that the SVM method achieved a higher accuracy rate compared to Naïve Bayes in detecting negative sentiments related to bullying toward teachers. These findings contribute to a better understanding of digital bullying patterns targeting educators and provide a foundation for developing more effective policies to address bullying cases in the educational environment. Keywords: Sentiment Analysis, Bullying, Teachers, Support Vector Machine, Naïve Bayes, Text Mining. Abstrak: Penelitian ini membahas analisis sentimen terhadap perundungan yang dialami oleh guru di media sosial. penelitian ini menggunakan metode support vector machine (svm) dan naïve bayes untuk mengklasifikasikan sentimen menjadi positif, negatif, atau netral. data yang digunakan berasal dari berbagai platform media sosial dan dianalisis menggunakan teknik text mining. hasil penelitian menunjukkan bahwa metode svm memiliki tingkat akurasi yang lebih tinggi dibandingkan dengan naïve bayes dalam mendeteksi sentimen negatif terkait perundungan terhadap guru. temuan ini dapat membantu dalam memahami pola perundungan digital terhadap tenaga pendidik serta memberikan dasar untuk kebijakan yang lebih efektif dalam menangani kasus perundungan di dunia pendidikan. Kata Kunci: Analisis Sentimen, Perundungan, Guru, Support Vector Machine, Naïve Bayes, Text Mining.
The Mitigating Overfitting in Sentiment Analysis Insights from CNN-LSTM Hybrid Models Susandri Susandri; Ahmad Zamsuri; Nurliana Nasution; Yoyon Efendi; Hiba Basim Alwan
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 2 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i2.4742

Abstract

This study aims to improve sentiment analysis accuracy and address overfitting challenges in deep learning models by developing a hybrid model based on Convolutional Neural Networks and Long Short-Term Memory Networks. The research methodology involved multiple stages, starting with preprocessing a dataset of 5,456 rows. This process included removing duplicate data, empty entries, and neutral sentiments, resulting in 2,685 usable rows. To overcome data quantity limitations, data augmentation expanded the training dataset from 2,148 to 10,740 samples. Data transformation was carried out using tokenization, padding, and embedding techniques, leveraging Word2Vec and GloVe to produce numerical representations of textual data. The hybrid model demonstrated strong performance, achieving a training accuracy of 99.51%, validation accuracy of 99.25%, and testing accuracy of 87.34%, with a loss value of 0.56. Evaluation metrics showed precision, recall, and F1-Score values of 86%, 87%, and 86%, respectively. The hybrid model outperformed individual models, including Convolutional Neural Networks (70% accuracy) and Long Short-Term Memory Networks (81% accuracy). It also surpassed other hybrid models, such as the multiscale Convolutional Neural Network-Long Short-Term Memory Network, which achieved a maximum accuracy of 89.25%. The implications of this study demonstrate that the hybrid model based on Convolutional Neural Networks and Long Short-Term Memory Networks effectively improves sentiment analysis accuracy while reducing the risk of overfitting, particularly in small or imbalanced datasets. Future research is recommended to enhance data quality, adopt more advanced embedding techniques, and optimize model configurations to achieve better performance.
Peran Mediasi Pelatihan Pada Kompetensi Pegawai Terhadap Kinerja  Pada Dinas Perdagangan Dan Perindustrian Kabupaten Bengkalis Marliya, Marliya; Afriana Munthe , Richa; Susandri, Susandri
New Economy Vol. 1 No. 1 (2025)
Publisher : CV. Akira Java Bulu

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study aims to Analyze the Influence of Competence on Performance through Employee Training at the Bengkalis Regency Trade and Industry Service. The research method used is quantitative research. all ASN employees of the Bengkalis Regency Trade and Industry Service totaling 58 people and sampling was carried out using census techniques. The data collection technique used was a structured questionnaire. The instrument testing technique in this study was the validity and reliability test using PLS (Partial Least Square) as its measurement. The results of this study indicate that Competence and training have a significant effect on employee performance. Competence also has a significant effect on employee training. In addition, Competence also has a positive effect on employee performance mediated by training.
Ekplorasi Timeline : Waktu Respon Pesan Terbaik WhatSapp Group “Gurauan kita STMIK Amik” Susandri susandri; Sarjon Defit; Fristi Riandari; Bosker Sinaga
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 20 No. 2 (2021)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v20i2.1149

Abstract

WhatsApp merupakan salah satu aplikasi pesan instan yang banyak di gunakan saat ini. WhatsApp memungkinkan pengguna membuat grup. Sering pesan pada grup tidak terbaca dan terabaikan oleh anggota grup. Perlu dilakukan analisa waktu yang tepat sebuah pesan direspon anggota grup dengan cepat sehingga informasi dapat disampaikan dengan baik pada semua anggota. Penelitian ini melakukan explorasi WhatSapp Group “Gurauan kita STMIK Amik” untuk menentukan waktu terbaik menyampaikan pesan dengan metode timeline serta menganalisis anggota yg berjumlah 32 orang, emoji dan sentimen. Pada Analisis sentimen dari 1095 total pesan, sentimen positif 35.53% dan sentimen negatif 64.47%. Respon emoji dari anggota sebanyak 46% menggunakan pesan emoji diatas 50% dan 34% anggota menggunakan emoji dibawah 50% sedangkan 18 % anggota tidak pernah menggunakan emoji. Dalam penelitian ini dari proses timeline dapat disimpulkan waktu terbaik untuk mengirimkan pesan pada hari selasa dan jum’at pada jam 10, 13 sampai 15 siang dan jam 20 pada malam hari.
Co-Authors -, Henry Adi Wansyah Afriana Munthe , Richa Agustin Ahmad Zamsuri Ahmad Zamsuri, Ahmad Alwan, Hiba Basim Anam, M Khairul Anwar Siregar Asparizal Asparizal, Asparizal Bakri Nasution, Feldiansyah Bimby, Novia Putri Bosker Sinaga Delpa Mario Desinawati, Desinawati Edwar Ali Efrizoni, Lusiana Endardhi, Azmi Rahmat Erlin Fajrizal Fajrizal, Fajrizal Feby Budi Setiawan Feldiansyah, Feldiansyah Fristi Riandari Fristi Riandari Hamdani Handayani , Risna Handayani, Risna Henry - Herwin -, Herwin Herwin Herwin, Herwin Hiba Basim Alwan Hidaya Spitri Indra Prayogo Indra Prayogo Istithoatun Kholishoh Karpen Khusaeri Andesa Koko Harianto Koko Harianto, Koko Mardainis Mardainis -, Mardainis Marliya, Marliya Melgisaputra Dwi Nanda Melgisaputra Dwi Nanda, Melgisaputra Dwi Muhammad Arief Solihin MUHAMMAD TAJUDDIN Nora Lizarti Nurjayadi Nurjayadi Nurliana Nasution Nurliana Nasution, Nurliana Pane, Eddissyah Putra Rahmaddeni Rahmaddeni Rahman, Bilhaki Rahmiati Rahmiati Rahmiati Ramadhani, Maya Ratna Yulia Rokib, Amul Salsabila Rabbani Sandi Saputra Sandi Saputra Sari Herlina Sarjon Defit Sarjon Defit Sumijan , Sumijan SUSANTI SUSI ERLINDA Triyani Arita Fitri Tutut Setiarini Ulfah, Aniq Noviciate Wahyuni, Neni Wansyah, Adi Yoyon Efendi Yoyon Efendi Yulia, Ratna Yurnalis